epoch_end = epoch_start
epoch_prev = epoch_start - 1
assert epoch_prev >= 0
# device
use_gpu_if_available = True
use_CUDA = use_gpu_if_available and torch.cuda.is_available()
if use_CUDA:
device = torch.device('cuda')
else:
device = torch.device('cpu')
print('# using', device)
# loading VGG11-bn pretrained model
#vgg16 = torchvision.models.vgg16(pretrained=False)
vgg16 = DCTnet.DCTnet2v2()
print(vgg16)
# loading the parameters
fnParam_prev = f'data/ex20210103_trainDCT2v2_epoch{epoch_prev:03d}.pth'
if epoch_prev != 0:
with open(fnParam_prev, mode='rb') as f:
vgg16.load_state_dict(torch.load(f))
nn = vgg16.to(device)
nn.train()
# dataset & dataloader
dL = ilsvrc2012.datasets('L')
bsize = 256
dl = torch.utils.data.DataLoader(dL,
import sys
import ilsvrc2012
import DCTnet
if __name__ == '__main__':
if len(sys.argv) != 2:
print(f'usage: {sys.argv[0]} epoch')
exit()
epoch = int(sys.argv[1])
# loading VGG16 model
#vgg16 = torchvision.models.vgg16(pretrained=False)
vgg16 = DCTnet.DCTnet2()
print(vgg16)
# loading the parameters
fnParam = f'data/ex20201228b_trainDCT2_epoch{epoch:03d}.pth'
with open(fnParam, mode='rb') as f:
vgg16.load_state_dict(torch.load(f))
# device
use_gpu_if_available = True
use_CUDA = use_gpu_if_available and torch.cuda.is_available()
if use_CUDA:
device = torch.device('cuda')
else:
device = torch.device('cpu')
print('# using', device)
import sys
import ilsvrc2012
import DCTnet
if __name__ == '__main__':
if len(sys.argv) != 2:
print(f'usage: {sys.argv[0]} epoch')
exit()
epoch = int(sys.argv[1])
# loading VGG16 model
#vgg16 = torchvision.models.vgg16(pretrained=False)
vgg16 = DCTnet.DCTnet()
print(vgg16)
# loading the parameters
fnParam = f'data/ex20201228_trainDCT_epoch{epoch:03d}.pth'
with open(fnParam, mode='rb') as f:
vgg16.load_state_dict(torch.load(f))
# device
use_gpu_if_available = True
use_CUDA = use_gpu_if_available and torch.cuda.is_available()
if use_CUDA:
device = torch.device('cuda')
else:
device = torch.device('cpu')
print('# using', device)
import ilsvrc2012
import DCTnet
if __name__ == '__main__':
if len(sys.argv) != 2:
print(f'usage: {sys.argv[0]} epoch')
exit()
epoch = int(sys.argv[1])
# loading VGG16 model
#vgg16 = torchvision.models.vgg16(pretrained=False)
vgg16 = DCTnet.DCTnet3()
print(vgg16)
# loading the parameters
fnParam = f'data/ex20201228c_trainDCT3_epoch{epoch:03d}.pth'
with open(fnParam, mode='rb') as f:
vgg16.load_state_dict(torch.load(f))
# device
use_gpu_if_available = True
use_CUDA = use_gpu_if_available and torch.cuda.is_available()
if use_CUDA:
device = torch.device('cuda')
else:
device = torch.device('cpu')
print('# using', device)